Overview

Dataset statistics

Number of variables31
Number of observations41101
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.7 MiB
Average record size in memory248.0 B

Variable types

Categorical17
Numeric14

Alerts

gdename_anfang has a high cardinality: 873 distinct values High cardinality
gdename_ende has a high cardinality: 743 distinct values High cardinality
hectarcoords_anfang has a high cardinality: 8146 distinct values High cardinality
hectarcoords_ende has a high cardinality: 6823 distinct values High cardinality
sex_anfang is highly correlated with sex_endHigh correlation
sex_end is highly correlated with sex_anfangHigh correlation
maritalstatus_anfang is highly correlated with maritalstatus_ende and 2 other fieldsHigh correlation
maritalstatus_ende is highly correlated with maritalstatus_anfang and 2 other fieldsHigh correlation
agegroup_anfang is highly correlated with maritalstatus_anfang and 2 other fieldsHigh correlation
agegroup_ende is highly correlated with maritalstatus_anfang and 2 other fieldsHigh correlation
gdektnr_anfang is highly correlated with umzugsdist and 2 other fieldsHigh correlation
gdektnr_ende is highly correlated with umzugsdist and 2 other fieldsHigh correlation
gdenr_anfang is highly correlated with umzugsdist and 2 other fieldsHigh correlation
gdenr_ende is highly correlated with umzugsdist and 2 other fieldsHigh correlation
umzugsdist is highly correlated with gdekt_anfang and 5 other fieldsHigh correlation
wazimsgroup_ende is highly correlated with gkats_ende and 1 other fieldsHigh correlation
wazimsgroup_anfang is highly correlated with gkats_anfang and 1 other fieldsHigh correlation
wareasgroup_anfang is highly correlated with wazimsgroup_anfangHigh correlation
wareasgroup_ende is highly correlated with wazimsgroup_endeHigh correlation
nationalitygroup_ende is highly correlated with nationalitygroup_anfangHigh correlation
nationalitygroup_anfang is highly correlated with nationalitygroup_endeHigh correlation
gdekt_anfang is highly correlated with umzugsdist and 2 other fieldsHigh correlation
gdekt_ende is highly correlated with umzugsdist and 2 other fieldsHigh correlation
gkats_anfang is highly correlated with wazimsgroup_anfangHigh correlation
gkats_ende is highly correlated with wazimsgroup_endeHigh correlation
agegroup_anfang has 607 (1.5%) zeros Zeros

Reproduction

Analysis started2022-09-29 07:25:52.825853
Analysis finished2022-09-29 07:26:19.671890
Duration26.85 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

rjhr
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.2 KiB
2019
13943 
2020
13690 
2018
13468 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters164404
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
201913943
33.9%
202013690
33.3%
201813468
32.8%

Length

2022-09-29T09:26:20.113249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:26:20.212362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
201913943
33.9%
202013690
33.3%
201813468
32.8%

Most occurring characters

ValueCountFrequency (%)
254791
33.3%
054791
33.3%
127411
16.7%
913943
 
8.5%
813468
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number164404
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
254791
33.3%
054791
33.3%
127411
16.7%
913943
 
8.5%
813468
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Common164404
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
254791
33.3%
054791
33.3%
127411
16.7%
913943
 
8.5%
813468
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII164404
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
254791
33.3%
054791
33.3%
127411
16.7%
913943
 
8.5%
813468
 
8.2%

persnr
Real number (ℝ≥0)

Distinct41041
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4189239.995
Minimum506
Maximum8409279
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.2 KiB
2022-09-29T09:26:20.307107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum506
5-th percentile428154
Q12111300
median4191746
Q36284656
95-th percentile7932119
Maximum8409279
Range8408773
Interquartile range (IQR)4173356

Descriptive statistics

Standard deviation2410166.174
Coefficient of variation (CV)0.5753230124
Kurtosis-1.200926012
Mean4189239.995
Median Absolute Deviation (MAD)2086690
Skewness-0.005391909919
Sum1.721819531 × 1011
Variance5.808900986 × 1012
MonotonicityNot monotonic
2022-09-29T09:26:20.410546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4895682
 
< 0.1%
11282522
 
< 0.1%
41963962
 
< 0.1%
77157972
 
< 0.1%
56390032
 
< 0.1%
45036802
 
< 0.1%
8282952
 
< 0.1%
80491862
 
< 0.1%
48609882
 
< 0.1%
27280682
 
< 0.1%
Other values (41031)41081
> 99.9%
ValueCountFrequency (%)
5061
< 0.1%
5981
< 0.1%
7511
< 0.1%
9991
< 0.1%
10021
< 0.1%
10451
< 0.1%
11131
< 0.1%
11621
< 0.1%
11941
< 0.1%
29301
< 0.1%
ValueCountFrequency (%)
84092791
< 0.1%
84085241
< 0.1%
84069101
< 0.1%
84053631
< 0.1%
84043891
< 0.1%
84035881
< 0.1%
84035601
< 0.1%
84029191
< 0.1%
84027661
< 0.1%
84024111
< 0.1%

umzugsdist
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16584
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15278.53429
Minimum0
Maximum196384
Zeros123
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size321.2 KiB
2022-09-29T09:26:20.524916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile159
Q11344
median3739
Q318742
95-th percentile70138
Maximum196384
Range196384
Interquartile range (IQR)17398

Descriptive statistics

Standard deviation24980.57866
Coefficient of variation (CV)1.63501146
Kurtosis9.020624462
Mean15278.53429
Median Absolute Deviation (MAD)3266
Skewness2.711790216
Sum627963038
Variance624029310.1
MonotonicityNot monotonic
2022-09-29T09:26:20.635906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0123
 
0.3%
4343
 
0.1%
5637
 
0.1%
5834
 
0.1%
3533
 
0.1%
10331
 
0.1%
13931
 
0.1%
1729
 
0.1%
3329
 
0.1%
6128
 
0.1%
Other values (16574)40683
99.0%
ValueCountFrequency (%)
0123
0.3%
24
 
< 0.1%
61
 
< 0.1%
98
 
< 0.1%
101
 
< 0.1%
113
 
< 0.1%
128
 
< 0.1%
134
 
< 0.1%
142
 
< 0.1%
1515
 
< 0.1%
ValueCountFrequency (%)
1963841
 
< 0.1%
1952491
 
< 0.1%
1951042
< 0.1%
1947143
< 0.1%
1942981
 
< 0.1%
1933221
 
< 0.1%
1932142
< 0.1%
1931471
 
< 0.1%
1931202
< 0.1%
1930471
 
< 0.1%

sex_anfang
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.2 KiB
2
20761 
1
20340 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41101
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
220761
50.5%
120340
49.5%

Length

2022-09-29T09:26:20.740160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:26:20.825205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
220761
50.5%
120340
49.5%

Most occurring characters

ValueCountFrequency (%)
220761
50.5%
120340
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
220761
50.5%
120340
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common41101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
220761
50.5%
120340
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
220761
50.5%
120340
49.5%

sex_end
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.2 KiB
2
20763 
1
20338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters41101
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
220763
50.5%
120338
49.5%

Length

2022-09-29T09:26:20.900426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:26:20.991601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
220763
50.5%
120338
49.5%

Most occurring characters

ValueCountFrequency (%)
220763
50.5%
120338
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
220763
50.5%
120338
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common41101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
220763
50.5%
120338
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
220763
50.5%
120338
49.5%

maritalstatus_anfang
Real number (ℝ)

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.458942605
Minimum-9
Maximum7
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)< 0.1%
Memory size321.2 KiB
2022-09-29T09:26:21.056343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum7
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8577394276
Coefficient of variation (CV)0.5879185547
Kurtosis6.771299167
Mean1.458942605
Median Absolute Deviation (MAD)0
Skewness2.098067328
Sum59964
Variance0.7357169257
MonotonicityNot monotonic
2022-09-29T09:26:21.129372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
128529
69.4%
29316
 
22.7%
42648
 
6.4%
3469
 
1.1%
6115
 
0.3%
718
 
< 0.1%
-93
 
< 0.1%
53
 
< 0.1%
ValueCountFrequency (%)
-93
 
< 0.1%
128529
69.4%
29316
 
22.7%
3469
 
1.1%
42648
 
6.4%
53
 
< 0.1%
6115
 
0.3%
718
 
< 0.1%
ValueCountFrequency (%)
718
 
< 0.1%
6115
 
0.3%
53
 
< 0.1%
42648
 
6.4%
3469
 
1.1%
29316
 
22.7%
128529
69.4%
-93
 
< 0.1%

maritalstatus_ende
Real number (ℝ)

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.496362619
Minimum-9
Maximum7
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size321.2 KiB
2022-09-29T09:26:21.213363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum7
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8857716365
Coefficient of variation (CV)0.5919498558
Kurtosis5.087653243
Mean1.496362619
Median Absolute Deviation (MAD)0
Skewness2.091130442
Sum61502
Variance0.784591392
MonotonicityNot monotonic
2022-09-29T09:26:21.290322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
127713
67.4%
29768
 
23.8%
42953
 
7.2%
3523
 
1.3%
6112
 
0.3%
727
 
0.1%
54
 
< 0.1%
-91
 
< 0.1%
ValueCountFrequency (%)
-91
 
< 0.1%
127713
67.4%
29768
 
23.8%
3523
 
1.3%
42953
 
7.2%
54
 
< 0.1%
6112
 
0.3%
727
 
0.1%
ValueCountFrequency (%)
727
 
0.1%
6112
 
0.3%
54
 
< 0.1%
42953
 
7.2%
3523
 
1.3%
29768
 
23.8%
127713
67.4%
-91
 
< 0.1%

agegroup_anfang
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.08374492
Minimum0
Maximum95
Zeros607
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size321.2 KiB
2022-09-29T09:26:21.393953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q120
median30
Q335
95-th percentile60
Maximum95
Range95
Interquartile range (IQR)15

Descriptive statistics

Standard deviation15.60617177
Coefficient of variation (CV)0.5187576152
Kurtosis0.9730733099
Mean30.08374492
Median Absolute Deviation (MAD)10
Skewness0.6939285214
Sum1236472
Variance243.5525973
MonotonicityNot monotonic
2022-09-29T09:26:21.489201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
259162
22.3%
306980
17.0%
205225
12.7%
354316
10.5%
402309
 
5.6%
451765
 
4.3%
501631
 
4.0%
151395
 
3.4%
551269
 
3.1%
60898
 
2.2%
Other values (18)6151
15.0%
ValueCountFrequency (%)
0607
1.5%
1641
1.6%
2459
1.1%
3406
1.0%
4310
0.8%
5296
0.7%
6226
 
0.5%
7222
 
0.5%
8200
 
0.5%
9190
 
0.5%
ValueCountFrequency (%)
953
 
< 0.1%
9017
 
< 0.1%
85123
 
0.3%
80194
 
0.5%
75271
 
0.7%
70482
 
1.2%
65653
1.6%
60898
2.2%
551269
3.1%
501631
4.0%

agegroup_ende
Real number (ℝ≥0)

HIGH CORRELATION

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.09508284
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.2 KiB
2022-09-29T09:26:21.581633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q125
median30
Q340
95-th percentile60
Maximum100
Range99
Interquartile range (IQR)15

Descriptive statistics

Standard deviation15.60957846
Coefficient of variation (CV)0.5019950754
Kurtosis0.9756884897
Mean31.09508284
Median Absolute Deviation (MAD)5
Skewness0.6892595266
Sum1278039
Variance243.6589396
MonotonicityNot monotonic
2022-09-29T09:26:21.663845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
258887
21.6%
307588
18.5%
354880
11.9%
204131
10.1%
402624
 
6.4%
451776
 
4.3%
501678
 
4.1%
551357
 
3.3%
151088
 
2.6%
60935
 
2.3%
Other values (18)6157
15.0%
ValueCountFrequency (%)
1607
1.5%
2641
1.6%
3459
1.1%
4406
1.0%
5310
 
0.8%
6296
 
0.7%
7226
 
0.5%
8222
 
0.5%
9200
 
0.5%
10877
2.1%
ValueCountFrequency (%)
1001
 
< 0.1%
955
 
< 0.1%
9027
 
0.1%
85141
 
0.3%
80208
 
0.5%
75304
 
0.7%
70528
 
1.3%
65699
1.7%
60935
2.3%
551357
3.3%

nationalitygroup_anfang
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.2 KiB
Switzerland
29422 
Other
3987 
Central Europe
3619 
Southern Europe
 
2166
Southeastern Europe
 
1010
Other values (3)
 
897

Length

Max length19
Median length11
Mean length11.15924187
Min length5

Characters and Unicode

Total characters458656
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSwitzerland
2nd rowSwitzerland
3rd rowSwitzerland
4th rowSwitzerland
5th rowCentral Europe

Common Values

ValueCountFrequency (%)
Switzerland29422
71.6%
Other3987
 
9.7%
Central Europe3619
 
8.8%
Southern Europe2166
 
5.3%
Southeastern Europe1010
 
2.5%
Western Europe590
 
1.4%
Northern Europe175
 
0.4%
Eastern Europe132
 
0.3%

Length

2022-09-29T09:26:21.748984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:26:21.847255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
switzerland29422
60.3%
europe7692
 
15.8%
other3987
 
8.2%
central3619
 
7.4%
southern2166
 
4.4%
southeastern1010
 
2.1%
western590
 
1.2%
northern175
 
0.4%
eastern132
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e50393
11.0%
r48968
10.7%
t42111
9.2%
n37114
8.1%
a34183
 
7.5%
l33041
 
7.2%
S32598
 
7.1%
z29422
 
6.4%
i29422
 
6.4%
d29422
 
6.4%
Other values (12)91982
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter402171
87.7%
Uppercase Letter48793
 
10.6%
Space Separator7692
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e50393
12.5%
r48968
12.2%
t42111
10.5%
n37114
9.2%
a34183
8.5%
l33041
8.2%
z29422
7.3%
i29422
7.3%
d29422
7.3%
w29422
7.3%
Other values (5)38673
9.6%
Uppercase Letter
ValueCountFrequency (%)
S32598
66.8%
E7824
 
16.0%
O3987
 
8.2%
C3619
 
7.4%
W590
 
1.2%
N175
 
0.4%
Space Separator
ValueCountFrequency (%)
7692
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin450964
98.3%
Common7692
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e50393
11.2%
r48968
10.9%
t42111
9.3%
n37114
8.2%
a34183
7.6%
l33041
 
7.3%
S32598
 
7.2%
z29422
 
6.5%
i29422
 
6.5%
d29422
 
6.5%
Other values (11)84290
18.7%
Common
ValueCountFrequency (%)
7692
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII458656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e50393
11.0%
r48968
10.7%
t42111
9.2%
n37114
8.1%
a34183
 
7.5%
l33041
 
7.2%
S32598
 
7.1%
z29422
 
6.4%
i29422
 
6.4%
d29422
 
6.4%
Other values (12)91982
20.1%

nationalitygroup_ende
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.2 KiB
Switzerland
29628 
Other
3874 
Central Europe
3586 
Southern Europe
 
2138
Southeastern Europe
 
979
Other values (3)
 
896

Length

Max length19
Median length11
Mean length11.16452154
Min length5

Characters and Unicode

Total characters458873
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSwitzerland
2nd rowSwitzerland
3rd rowSwitzerland
4th rowSwitzerland
5th rowCentral Europe

Common Values

ValueCountFrequency (%)
Switzerland29628
72.1%
Other3874
 
9.4%
Central Europe3586
 
8.7%
Southern Europe2138
 
5.2%
Southeastern Europe979
 
2.4%
Western Europe592
 
1.4%
Northern Europe176
 
0.4%
Eastern Europe128
 
0.3%

Length

2022-09-29T09:26:21.944060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:26:22.051122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
switzerland29628
60.8%
europe7599
 
15.6%
other3874
 
8.0%
central3586
 
7.4%
southern2138
 
4.4%
southeastern979
 
2.0%
western592
 
1.2%
northern176
 
0.4%
eastern128
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e50271
11.0%
r48876
10.7%
t42080
9.2%
n37227
8.1%
a34321
 
7.5%
l33214
 
7.2%
S32745
 
7.1%
z29628
 
6.5%
i29628
 
6.5%
d29628
 
6.5%
Other values (12)91255
19.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter402574
87.7%
Uppercase Letter48700
 
10.6%
Space Separator7599
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e50271
12.5%
r48876
12.1%
t42080
10.5%
n37227
9.2%
a34321
8.5%
l33214
8.3%
z29628
7.4%
i29628
7.4%
d29628
7.4%
w29628
7.4%
Other values (5)38073
9.5%
Uppercase Letter
ValueCountFrequency (%)
S32745
67.2%
E7727
 
15.9%
O3874
 
8.0%
C3586
 
7.4%
W592
 
1.2%
N176
 
0.4%
Space Separator
ValueCountFrequency (%)
7599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin451274
98.3%
Common7599
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e50271
11.1%
r48876
10.8%
t42080
9.3%
n37227
8.2%
a34321
7.6%
l33214
 
7.4%
S32745
 
7.3%
z29628
 
6.6%
i29628
 
6.6%
d29628
 
6.6%
Other values (11)83656
18.5%
Common
ValueCountFrequency (%)
7599
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII458873
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e50271
11.0%
r48876
10.7%
t42080
9.2%
n37227
8.1%
a34321
 
7.5%
l33214
 
7.2%
S32745
 
7.1%
z29628
 
6.5%
i29628
 
6.5%
d29628
 
6.5%
Other values (12)91255
19.9%

gdekt_anfang
Categorical

HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size321.2 KiB
LU
35816 
ZH
 
1034
AG
 
645
BE
 
527
NW
 
460
Other values (21)
 
2619

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters82202
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLU
2nd rowLU
3rd rowLU
4th rowLU
5th rowLU

Common Values

ValueCountFrequency (%)
LU35816
87.1%
ZH1034
 
2.5%
AG645
 
1.6%
BE527
 
1.3%
NW460
 
1.1%
ZG438
 
1.1%
OW346
 
0.8%
SZ330
 
0.8%
SG217
 
0.5%
SO192
 
0.5%
Other values (16)1096
 
2.7%

Length

2022-09-29T09:26:22.147650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lu35816
87.1%
zh1034
 
2.5%
ag645
 
1.6%
be527
 
1.3%
nw460
 
1.1%
zg438
 
1.1%
ow346
 
0.8%
sz330
 
0.8%
sg217
 
0.5%
so192
 
0.5%
Other values (16)1096
 
2.7%

Most occurring characters

ValueCountFrequency (%)
L35953
43.7%
U35929
43.7%
Z1802
 
2.2%
G1587
 
1.9%
H1063
 
1.3%
S1009
 
1.2%
W806
 
1.0%
B789
 
1.0%
A671
 
0.8%
E573
 
0.7%
Other values (9)2020
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter82202
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L35953
43.7%
U35929
43.7%
Z1802
 
2.2%
G1587
 
1.9%
H1063
 
1.3%
S1009
 
1.2%
W806
 
1.0%
B789
 
1.0%
A671
 
0.8%
E573
 
0.7%
Other values (9)2020
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin82202
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L35953
43.7%
U35929
43.7%
Z1802
 
2.2%
G1587
 
1.9%
H1063
 
1.3%
S1009
 
1.2%
W806
 
1.0%
B789
 
1.0%
A671
 
0.8%
E573
 
0.7%
Other values (9)2020
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII82202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L35953
43.7%
U35929
43.7%
Z1802
 
2.2%
G1587
 
1.9%
H1063
 
1.3%
S1009
 
1.2%
W806
 
1.0%
B789
 
1.0%
A671
 
0.8%
E573
 
0.7%
Other values (9)2020
 
2.5%

gdekt_ende
Categorical

HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size321.2 KiB
LU
36189 
ZH
 
1402
AG
 
538
BE
 
499
ZG
 
425
Other values (21)
 
2048

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters82202
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLU
2nd rowLU
3rd rowLU
4th rowLU
5th rowLU

Common Values

ValueCountFrequency (%)
LU36189
88.0%
ZH1402
 
3.4%
AG538
 
1.3%
BE499
 
1.2%
ZG425
 
1.0%
NW399
 
1.0%
SZ337
 
0.8%
OW261
 
0.6%
BS199
 
0.5%
SO125
 
0.3%
Other values (16)727
 
1.8%

Length

2022-09-29T09:26:22.230681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lu36189
88.0%
zh1402
 
3.4%
ag538
 
1.3%
be499
 
1.2%
zg425
 
1.0%
nw399
 
1.0%
sz337
 
0.8%
ow261
 
0.6%
bs199
 
0.5%
so125
 
0.3%
Other values (16)727
 
1.8%

Most occurring characters

ValueCountFrequency (%)
L36292
44.1%
U36262
44.1%
Z2164
 
2.6%
H1419
 
1.7%
G1270
 
1.5%
S821
 
1.0%
B789
 
1.0%
W660
 
0.8%
A552
 
0.7%
E520
 
0.6%
Other values (9)1453
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter82202
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L36292
44.1%
U36262
44.1%
Z2164
 
2.6%
H1419
 
1.7%
G1270
 
1.5%
S821
 
1.0%
B789
 
1.0%
W660
 
0.8%
A552
 
0.7%
E520
 
0.6%
Other values (9)1453
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Latin82202
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L36292
44.1%
U36262
44.1%
Z2164
 
2.6%
H1419
 
1.7%
G1270
 
1.5%
S821
 
1.0%
B789
 
1.0%
W660
 
0.8%
A552
 
0.7%
E520
 
0.6%
Other values (9)1453
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII82202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L36292
44.1%
U36262
44.1%
Z2164
 
2.6%
H1419
 
1.7%
G1270
 
1.5%
S821
 
1.0%
B789
 
1.0%
W660
 
0.8%
A552
 
0.7%
E520
 
0.6%
Other values (9)1453
 
1.8%

gdektnr_anfang
Real number (ℝ≥0)

HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.794311574
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.2 KiB
2022-09-29T09:26:22.308229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median3
Q33
95-th percentile9
Maximum26
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.305753703
Coefficient of variation (CV)0.8712393904
Kurtosis17.09921992
Mean3.794311574
Median Absolute Deviation (MAD)0
Skewness4.153387168
Sum155950
Variance10.92800754
MonotonicityNot monotonic
2022-09-29T09:26:22.392655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
335816
87.1%
11034
 
2.5%
19645
 
1.6%
2527
 
1.3%
7460
 
1.1%
9438
 
1.1%
6346
 
0.8%
5330
 
0.8%
17217
 
0.5%
11192
 
0.5%
Other values (16)1096
 
2.7%
ValueCountFrequency (%)
11034
 
2.5%
2527
 
1.3%
335816
87.1%
4107
 
0.3%
5330
 
0.8%
6346
 
0.8%
7460
 
1.1%
823
 
0.1%
9438
 
1.1%
1069
 
0.2%
ValueCountFrequency (%)
266
 
< 0.1%
2534
 
0.1%
2412
 
< 0.1%
2393
 
0.2%
2279
 
0.2%
21126
 
0.3%
2076
 
0.2%
19645
1.6%
18154
 
0.4%
17217
 
0.5%

gdektnr_ende
Real number (ℝ≥0)

HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.57456023
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.2 KiB
2022-09-29T09:26:22.480027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median3
Q33
95-th percentile7
Maximum26
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.865605927
Coefficient of variation (CV)0.8016667066
Kurtosis23.16191557
Mean3.57456023
Median Absolute Deviation (MAD)0
Skewness4.739147296
Sum146918
Variance8.211697328
MonotonicityNot monotonic
2022-09-29T09:26:22.571580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
336189
88.0%
11402
 
3.4%
19538
 
1.3%
2499
 
1.2%
9425
 
1.0%
7399
 
1.0%
5337
 
0.8%
6261
 
0.6%
12199
 
0.5%
11125
 
0.3%
Other values (16)727
 
1.8%
ValueCountFrequency (%)
11402
 
3.4%
2499
 
1.2%
336189
88.0%
471
 
0.2%
5337
 
0.8%
6261
 
0.6%
7399
 
1.0%
812
 
< 0.1%
9425
 
1.0%
1027
 
0.1%
ValueCountFrequency (%)
262
 
< 0.1%
2513
 
< 0.1%
248
 
< 0.1%
2343
 
0.1%
2277
 
0.2%
2170
 
0.2%
2058
 
0.1%
19538
1.3%
18124
 
0.3%
17100
 
0.2%

gdenr_anfang
Real number (ℝ≥0)

HIGH CORRELATION

Distinct874
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1189.072334
Minimum1
Maximum6711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.2 KiB
2022-09-29T09:26:22.666090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1024
Q11061
median1061
Q31061
95-th percentile1711
Maximum6711
Range6710
Interquartile range (IQR)0

Descriptive statistics

Standard deviation686.6994676
Coefficient of variation (CV)0.5775085737
Kurtosis23.77067775
Mean1189.072334
Median Absolute Deviation (MAD)0
Skewness4.551282947
Sum48872062
Variance471556.1588
MonotonicityNot monotonic
2022-09-29T09:26:22.769799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106128736
69.9%
10241138
 
2.8%
10591105
 
2.7%
1058546
 
1.3%
261459
 
1.1%
1054438
 
1.1%
1063240
 
0.6%
1051207
 
0.5%
1062204
 
0.5%
1103194
 
0.5%
Other values (864)7834
 
19.1%
ValueCountFrequency (%)
11
 
< 0.1%
230
0.1%
32
 
< 0.1%
45
 
< 0.1%
54
 
< 0.1%
61
 
< 0.1%
77
 
< 0.1%
98
 
< 0.1%
108
 
< 0.1%
112
 
< 0.1%
ValueCountFrequency (%)
67112
 
< 0.1%
67084
 
< 0.1%
66341
 
< 0.1%
66332
 
< 0.1%
66316
 
< 0.1%
66281
 
< 0.1%
66232
 
< 0.1%
662119
< 0.1%
66171
 
< 0.1%
66141
 
< 0.1%

gdenr_ende
Real number (ℝ≥0)

HIGH CORRELATION

Distinct744
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1141.95713
Minimum1
Maximum6711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.2 KiB
2022-09-29T09:26:22.879090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1023
Q11061
median1061
Q31061
95-th percentile1509
Maximum6711
Range6710
Interquartile range (IQR)0

Descriptive statistics

Standard deviation592.5876274
Coefficient of variation (CV)0.5189228316
Kurtosis29.34013863
Mean1141.95713
Median Absolute Deviation (MAD)0
Skewness4.895028969
Sum46935580
Variance351160.0962
MonotonicityNot monotonic
2022-09-29T09:26:22.988777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106128050
68.2%
10591780
 
4.3%
10241424
 
3.5%
1058753
 
1.8%
261742
 
1.8%
1054676
 
1.6%
1063427
 
1.0%
1051298
 
0.7%
1040223
 
0.5%
1062207
 
0.5%
Other values (734)6521
 
15.9%
ValueCountFrequency (%)
11
 
< 0.1%
231
0.1%
34
 
< 0.1%
41
 
< 0.1%
55
 
< 0.1%
61
 
< 0.1%
73
 
< 0.1%
81
 
< 0.1%
98
 
< 0.1%
109
 
< 0.1%
ValueCountFrequency (%)
67112
 
< 0.1%
66301
 
< 0.1%
66231
 
< 0.1%
66216
< 0.1%
66173
< 0.1%
66161
 
< 0.1%
66051
 
< 0.1%
64591
 
< 0.1%
64585
< 0.1%
64211
 
< 0.1%

gdename_anfang
Categorical

HIGH CARDINALITY

Distinct873
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size321.2 KiB
Luzern
28736 
Emmen
 
1138
Kriens
 
1105
Horw
 
546
Zürich
 
459
Other values (868)
9117 

Length

Max length26
Median length6
Mean length6.435123233
Min length3

Characters and Unicode

Total characters264490
Distinct characters63
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique295 ?
Unique (%)0.7%

Sample

1st rowLuzern
2nd rowLuzern
3rd rowLuzern
4th rowLuzern
5th rowLuzern

Common Values

ValueCountFrequency (%)
Luzern28736
69.9%
Emmen1138
 
2.8%
Kriens1105
 
2.7%
Horw546
 
1.3%
Zürich459
 
1.1%
Ebikon438
 
1.1%
Meggen240
 
0.6%
Adligenswil207
 
0.5%
Malters204
 
0.5%
Sursee194
 
0.5%
Other values (863)7834
 
19.1%

Length

2022-09-29T09:26:23.102569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
luzern28736
68.2%
emmen1138
 
2.7%
kriens1105
 
2.6%
horw546
 
1.3%
zürich459
 
1.1%
ebikon438
 
1.0%
meggen240
 
0.6%
adligenswil207
 
0.5%
malters204
 
0.5%
sursee194
 
0.5%
Other values (874)8887
 
21.1%

Most occurring characters

ValueCountFrequency (%)
e38826
14.7%
n37694
14.3%
r35330
13.4%
u31075
11.7%
z29343
11.1%
L29133
11.0%
i6701
 
2.5%
s4861
 
1.8%
l4035
 
1.5%
h3744
 
1.4%
Other values (53)43748
16.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter218819
82.7%
Uppercase Letter42929
 
16.2%
Space Separator1053
 
0.4%
Open Punctuation705
 
0.3%
Close Punctuation705
 
0.3%
Dash Punctuation160
 
0.1%
Other Punctuation119
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e38826
17.7%
n37694
17.2%
r35330
16.1%
u31075
14.2%
z29343
13.4%
i6701
 
3.1%
s4861
 
2.2%
l4035
 
1.8%
h3744
 
1.7%
a3683
 
1.7%
Other values (22)23527
10.8%
Uppercase Letter
ValueCountFrequency (%)
L29133
67.9%
E2005
 
4.7%
S1451
 
3.4%
K1410
 
3.3%
H1201
 
2.8%
B1175
 
2.7%
R907
 
2.1%
Z891
 
2.1%
M806
 
1.9%
A728
 
1.7%
Other values (15)3222
 
7.5%
Other Punctuation
ValueCountFrequency (%)
.86
72.3%
/33
 
27.7%
Space Separator
ValueCountFrequency (%)
1053
100.0%
Open Punctuation
ValueCountFrequency (%)
(705
100.0%
Close Punctuation
ValueCountFrequency (%)
)705
100.0%
Dash Punctuation
ValueCountFrequency (%)
-160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin261748
99.0%
Common2742
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e38826
14.8%
n37694
14.4%
r35330
13.5%
u31075
11.9%
z29343
11.2%
L29133
11.1%
i6701
 
2.6%
s4861
 
1.9%
l4035
 
1.5%
h3744
 
1.4%
Other values (47)41006
15.7%
Common
ValueCountFrequency (%)
1053
38.4%
(705
25.7%
)705
25.7%
-160
 
5.8%
.86
 
3.1%
/33
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII263144
99.5%
None1346
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e38826
14.8%
n37694
14.3%
r35330
13.4%
u31075
11.8%
z29343
11.2%
L29133
11.1%
i6701
 
2.5%
s4861
 
1.8%
l4035
 
1.5%
h3744
 
1.4%
Other values (46)42402
16.1%
None
ValueCountFrequency (%)
ü1071
79.6%
ö150
 
11.1%
ä90
 
6.7%
è22
 
1.6%
â6
 
0.4%
é6
 
0.4%
ê1
 
0.1%

gdename_ende
Categorical

HIGH CARDINALITY

Distinct743
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size321.2 KiB
Luzern
28050 
Kriens
 
1780
Emmen
 
1424
Horw
 
753
Zürich
 
742
Other values (738)
8352 

Length

Max length26
Median length6
Mean length6.346098635
Min length3

Characters and Unicode

Total characters260831
Distinct characters62
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique259 ?
Unique (%)0.6%

Sample

1st rowLuzern
2nd rowLuzern
3rd rowLuzern
4th rowLuzern
5th rowLuzern

Common Values

ValueCountFrequency (%)
Luzern28050
68.2%
Kriens1780
 
4.3%
Emmen1424
 
3.5%
Horw753
 
1.8%
Zürich742
 
1.8%
Ebikon676
 
1.6%
Meggen427
 
1.0%
Adligenswil298
 
0.7%
Rothenburg223
 
0.5%
Malters207
 
0.5%
Other values (733)6521
 
15.9%

Length

2022-09-29T09:26:23.196315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
luzern28050
66.9%
kriens1780
 
4.2%
emmen1424
 
3.4%
horw753
 
1.8%
zürich742
 
1.8%
ebikon676
 
1.6%
meggen427
 
1.0%
adligenswil298
 
0.7%
rothenburg223
 
0.5%
malters207
 
0.5%
Other values (745)7372
 
17.6%

Most occurring characters

ValueCountFrequency (%)
e38517
14.8%
n37701
14.5%
r35187
13.5%
u30013
11.5%
z28447
10.9%
L28336
10.9%
i7494
 
2.9%
s5061
 
1.9%
l3572
 
1.4%
h3442
 
1.3%
Other values (52)43061
16.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter216025
82.8%
Uppercase Letter42573
 
16.3%
Space Separator851
 
0.3%
Open Punctuation601
 
0.2%
Close Punctuation601
 
0.2%
Dash Punctuation97
 
< 0.1%
Other Punctuation83
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e38517
17.8%
n37701
17.5%
r35187
16.3%
u30013
13.9%
z28447
13.2%
i7494
 
3.5%
s5061
 
2.3%
l3572
 
1.7%
h3442
 
1.6%
o3402
 
1.6%
Other values (21)23189
10.7%
Uppercase Letter
ValueCountFrequency (%)
L28336
66.6%
E2421
 
5.7%
K2097
 
4.9%
H1217
 
2.9%
S1209
 
2.8%
Z1143
 
2.7%
B1092
 
2.6%
M941
 
2.2%
R852
 
2.0%
A767
 
1.8%
Other values (14)2498
 
5.9%
Other Punctuation
ValueCountFrequency (%)
.45
54.2%
/37
44.6%
'1
 
1.2%
Space Separator
ValueCountFrequency (%)
851
100.0%
Open Punctuation
ValueCountFrequency (%)
(601
100.0%
Close Punctuation
ValueCountFrequency (%)
)601
100.0%
Dash Punctuation
ValueCountFrequency (%)
-97
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin258598
99.1%
Common2233
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e38517
14.9%
n37701
14.6%
r35187
13.6%
u30013
11.6%
z28447
11.0%
L28336
11.0%
i7494
 
2.9%
s5061
 
2.0%
l3572
 
1.4%
h3442
 
1.3%
Other values (45)40828
15.8%
Common
ValueCountFrequency (%)
851
38.1%
(601
26.9%
)601
26.9%
-97
 
4.3%
.45
 
2.0%
/37
 
1.7%
'1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII259399
99.5%
None1432
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e38517
14.8%
n37701
14.5%
r35187
13.6%
u30013
11.6%
z28447
11.0%
L28336
10.9%
i7494
 
2.9%
s5061
 
2.0%
l3572
 
1.4%
h3442
 
1.3%
Other values (46)41629
16.0%
None
ValueCountFrequency (%)
ü1217
85.0%
ä111
 
7.8%
ö83
 
5.8%
è8
 
0.6%
é7
 
0.5%
â6
 
0.4%

gkats_anfang
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.2 KiB
1025
28158 
1030
8626 
1021
3266 
1040
 
1051

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters164404
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1030
2nd row1030
3rd row1025
4th row1030
5th row1025

Common Values

ValueCountFrequency (%)
102528158
68.5%
10308626
 
21.0%
10213266
 
7.9%
10401051
 
2.6%

Length

2022-09-29T09:26:23.283444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:26:23.367061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
102528158
68.5%
10308626
 
21.0%
10213266
 
7.9%
10401051
 
2.6%

Most occurring characters

ValueCountFrequency (%)
050778
30.9%
144367
27.0%
231424
19.1%
528158
17.1%
38626
 
5.2%
41051
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number164404
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
050778
30.9%
144367
27.0%
231424
19.1%
528158
17.1%
38626
 
5.2%
41051
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common164404
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
050778
30.9%
144367
27.0%
231424
19.1%
528158
17.1%
38626
 
5.2%
41051
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII164404
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
050778
30.9%
144367
27.0%
231424
19.1%
528158
17.1%
38626
 
5.2%
41051
 
0.6%

gkats_ende
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.2 KiB
1025
28909 
1030
8908 
1021
 
2368
1040
 
916

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters164404
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1030
2nd row1025
3rd row1025
4th row1030
5th row1025

Common Values

ValueCountFrequency (%)
102528909
70.3%
10308908
 
21.7%
10212368
 
5.8%
1040916
 
2.2%

Length

2022-09-29T09:26:23.443379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:26:23.535288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
102528909
70.3%
10308908
 
21.7%
10212368
 
5.8%
1040916
 
2.2%

Most occurring characters

ValueCountFrequency (%)
050925
31.0%
143469
26.4%
231277
19.0%
528909
17.6%
38908
 
5.4%
4916
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number164404
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
050925
31.0%
143469
26.4%
231277
19.0%
528909
17.6%
38908
 
5.4%
4916
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common164404
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
050925
31.0%
143469
26.4%
231277
19.0%
528909
17.6%
38908
 
5.4%
4916
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII164404
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
050925
31.0%
143469
26.4%
231277
19.0%
528909
17.6%
38908
 
5.4%
4916
 
0.6%

gbaups_anfang
Real number (ℝ≥0)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8014.779129
Minimum8011
Maximum8023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.2 KiB
2022-09-29T09:26:23.603567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8011
5-th percentile8011
Q18012
median8014
Q38017
95-th percentile8022
Maximum8023
Range12
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.480410725
Coefficient of variation (CV)0.0004342491126
Kurtosis-0.3065580131
Mean8014.779129
Median Absolute Deviation (MAD)2
Skewness0.9315997705
Sum329415437
Variance12.11325881
MonotonicityNot monotonic
2022-09-29T09:26:23.678001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
80137285
17.7%
80116543
15.9%
80125803
14.1%
80145622
13.7%
80153899
9.5%
80222399
 
5.8%
80211667
 
4.1%
80191649
 
4.0%
80171474
 
3.6%
80181430
 
3.5%
Other values (3)3330
8.1%
ValueCountFrequency (%)
80116543
15.9%
80125803
14.1%
80137285
17.7%
80145622
13.7%
80153899
9.5%
80161279
 
3.1%
80171474
 
3.6%
80181430
 
3.5%
80191649
 
4.0%
80201207
 
2.9%
ValueCountFrequency (%)
8023844
 
2.1%
80222399
5.8%
80211667
 
4.1%
80201207
 
2.9%
80191649
 
4.0%
80181430
 
3.5%
80171474
 
3.6%
80161279
 
3.1%
80153899
9.5%
80145622
13.7%

gbaups_ende
Real number (ℝ≥0)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8015.538162
Minimum8011
Maximum8023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.2 KiB
2022-09-29T09:26:23.751591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8011
5-th percentile8011
Q18012
median8014
Q38019
95-th percentile8023
Maximum8023
Range12
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.035196105
Coefficient of variation (CV)0.0005034217321
Kurtosis-0.8885738112
Mean8015.538162
Median Absolute Deviation (MAD)2
Skewness0.7283602332
Sum329446634
Variance16.28280761
MonotonicityNot monotonic
2022-09-29T09:26:23.826965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
80136578
16.0%
80115705
13.9%
80145323
13.0%
80125048
12.3%
80234255
10.4%
80153950
9.6%
80222408
 
5.9%
80211660
 
4.0%
80161362
 
3.3%
80171351
 
3.3%
Other values (3)3461
8.4%
ValueCountFrequency (%)
80115705
13.9%
80125048
12.3%
80136578
16.0%
80145323
13.0%
80153950
9.6%
80161362
 
3.3%
80171351
 
3.3%
80181192
 
2.9%
80191279
 
3.1%
8020990
 
2.4%
ValueCountFrequency (%)
80234255
10.4%
80222408
5.9%
80211660
 
4.0%
8020990
 
2.4%
80191279
 
3.1%
80181192
 
2.9%
80171351
 
3.3%
80161362
 
3.3%
80153950
9.6%
80145323
13.0%

gazwot_anfang
Real number (ℝ≥0)

Distinct104
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.20525048
Minimum1
Maximum225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.2 KiB
2022-09-29T09:26:23.920517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q313
95-th percentile34
Maximum225
Range224
Interquartile range (IQR)9

Descriptive statistics

Standard deviation16.92473715
Coefficient of variation (CV)1.386676757
Kurtosis50.77429994
Mean12.20525048
Median Absolute Deviation (MAD)4
Skewness6.054119507
Sum501648
Variance286.4467277
MonotonicityNot monotonic
2022-09-29T09:26:24.025707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13975
 
9.7%
83584
 
8.7%
63108
 
7.6%
122628
 
6.4%
72414
 
5.9%
32399
 
5.8%
102260
 
5.5%
42206
 
5.4%
91981
 
4.8%
21860
 
4.5%
Other values (94)14686
35.7%
ValueCountFrequency (%)
13975
9.7%
21860
4.5%
32399
5.8%
42206
5.4%
51595
3.9%
63108
7.6%
72414
5.9%
83584
8.7%
91981
4.8%
102260
5.5%
ValueCountFrequency (%)
2251
 
< 0.1%
2201
 
< 0.1%
2151
 
< 0.1%
2142
 
< 0.1%
2021
 
< 0.1%
186119
0.3%
17920
 
< 0.1%
1601
 
< 0.1%
1551
 
< 0.1%
1504
 
< 0.1%

gazwot_ende
Real number (ℝ≥0)

Distinct107
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.26522469
Minimum1
Maximum258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.2 KiB
2022-09-29T09:26:24.128537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median9
Q315
95-th percentile46
Maximum258
Range257
Interquartile range (IQR)10

Descriptive statistics

Standard deviation24.37773594
Coefficient of variation (CV)1.596945766
Kurtosis29.30636641
Mean15.26522469
Median Absolute Deviation (MAD)5
Skewness5.029860694
Sum627416
Variance594.2740094
MonotonicityNot monotonic
2022-09-29T09:26:24.235781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83411
 
8.3%
63124
 
7.6%
12832
 
6.9%
122566
 
6.2%
72445
 
5.9%
102229
 
5.4%
32168
 
5.3%
92108
 
5.1%
42099
 
5.1%
111823
 
4.4%
Other values (97)16296
39.6%
ValueCountFrequency (%)
12832
6.9%
21665
4.1%
32168
5.3%
42099
5.1%
51541
3.7%
63124
7.6%
72445
5.9%
83411
8.3%
92108
5.1%
102229
5.4%
ValueCountFrequency (%)
2582
 
< 0.1%
2024
 
< 0.1%
186138
 
0.3%
179395
1.0%
1663
 
< 0.1%
1601
 
< 0.1%
14632
 
0.1%
145110
 
0.3%
1371
 
< 0.1%
1341
 
< 0.1%

wazimsgroup_anfang
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.2 KiB
3-4
26235 
5-6
7095 
1-2
6717 
>6
 
1054

Length

Max length3
Median length3
Mean length2.974355855
Min length2

Characters and Unicode

Total characters122249
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3-4
2nd row3-4
3rd row3-4
4th row1-2
5th row1-2

Common Values

ValueCountFrequency (%)
3-426235
63.8%
5-67095
 
17.3%
1-26717
 
16.3%
>61054
 
2.6%

Length

2022-09-29T09:26:24.331844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:26:24.420391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3-426235
63.8%
5-67095
 
17.3%
1-26717
 
16.3%
61054
 
2.6%

Most occurring characters

ValueCountFrequency (%)
-40047
32.8%
326235
21.5%
426235
21.5%
68149
 
6.7%
57095
 
5.8%
16717
 
5.5%
26717
 
5.5%
>1054
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number81148
66.4%
Dash Punctuation40047
32.8%
Math Symbol1054
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
326235
32.3%
426235
32.3%
68149
 
10.0%
57095
 
8.7%
16717
 
8.3%
26717
 
8.3%
Dash Punctuation
ValueCountFrequency (%)
-40047
100.0%
Math Symbol
ValueCountFrequency (%)
>1054
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common122249
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-40047
32.8%
326235
21.5%
426235
21.5%
68149
 
6.7%
57095
 
5.8%
16717
 
5.5%
26717
 
5.5%
>1054
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII122249
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-40047
32.8%
326235
21.5%
426235
21.5%
68149
 
6.7%
57095
 
5.8%
16717
 
5.5%
26717
 
5.5%
>1054
 
0.9%

wazimsgroup_ende
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.2 KiB
3-4
27055 
1-2
7221 
5-6
6037 
>6
 
788

Length

Max length3
Median length3
Mean length2.980827717
Min length2

Characters and Unicode

Total characters122515
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1-2
2nd row5-6
3rd row3-4
4th row1-2
5th row3-4

Common Values

ValueCountFrequency (%)
3-427055
65.8%
1-27221
 
17.6%
5-66037
 
14.7%
>6788
 
1.9%

Length

2022-09-29T09:26:24.505172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:26:24.598613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3-427055
65.8%
1-27221
 
17.6%
5-66037
 
14.7%
6788
 
1.9%

Most occurring characters

ValueCountFrequency (%)
-40313
32.9%
327055
22.1%
427055
22.1%
17221
 
5.9%
27221
 
5.9%
66825
 
5.6%
56037
 
4.9%
>788
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number81414
66.5%
Dash Punctuation40313
32.9%
Math Symbol788
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
327055
33.2%
427055
33.2%
17221
 
8.9%
27221
 
8.9%
66825
 
8.4%
56037
 
7.4%
Dash Punctuation
ValueCountFrequency (%)
-40313
100.0%
Math Symbol
ValueCountFrequency (%)
>788
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common122515
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-40313
32.9%
327055
22.1%
427055
22.1%
17221
 
5.9%
27221
 
5.9%
66825
 
5.6%
56037
 
4.9%
>788
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII122515
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-40313
32.9%
327055
22.1%
427055
22.1%
17221
 
5.9%
27221
 
5.9%
66825
 
5.6%
56037
 
4.9%
>788
 
0.6%

wareasgroup_anfang
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.2 KiB
70-99
15377 
100-149
11580 
50-69
6181 
>150
4244 
<50
3719 

Length

Max length7
Median length5
Mean length5.279263278
Min length3

Characters and Unicode

Total characters216983
Distinct characters10
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100-149
2nd row70-99
3rd row100-149
4th row<50
5th row70-99

Common Values

ValueCountFrequency (%)
70-9915377
37.4%
100-14911580
28.2%
50-696181
15.0%
>1504244
 
10.3%
<503719
 
9.0%

Length

2022-09-29T09:26:24.681696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:26:24.779777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
70-9915377
37.4%
100-14911580
28.2%
50-696181
15.0%
1504244
 
10.3%
503719
 
9.0%

Most occurring characters

ValueCountFrequency (%)
052681
24.3%
948515
22.4%
-33138
15.3%
127404
12.6%
715377
 
7.1%
514144
 
6.5%
411580
 
5.3%
66181
 
2.8%
>4244
 
2.0%
<3719
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number175882
81.1%
Dash Punctuation33138
 
15.3%
Math Symbol7963
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
052681
30.0%
948515
27.6%
127404
15.6%
715377
 
8.7%
514144
 
8.0%
411580
 
6.6%
66181
 
3.5%
Math Symbol
ValueCountFrequency (%)
>4244
53.3%
<3719
46.7%
Dash Punctuation
ValueCountFrequency (%)
-33138
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common216983
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
052681
24.3%
948515
22.4%
-33138
15.3%
127404
12.6%
715377
 
7.1%
514144
 
6.5%
411580
 
5.3%
66181
 
2.8%
>4244
 
2.0%
<3719
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII216983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
052681
24.3%
948515
22.4%
-33138
15.3%
127404
12.6%
715377
 
7.1%
514144
 
6.5%
411580
 
5.3%
66181
 
2.8%
>4244
 
2.0%
<3719
 
1.7%

wareasgroup_ende
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.2 KiB
70-99
15304 
100-149
12404 
50-69
6401 
>150
3513 
<50
3479 

Length

Max length7
Median length5
Mean length5.34882363
Min length3

Characters and Unicode

Total characters219842
Distinct characters10
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50-69
2nd row100-149
3rd row100-149
4th row50-69
5th row100-149

Common Values

ValueCountFrequency (%)
70-9915304
37.2%
100-14912404
30.2%
50-696401
15.6%
>1503513
 
8.5%
<503479
 
8.5%

Length

2022-09-29T09:26:24.865205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-29T09:26:24.959564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
70-9915304
37.2%
100-14912404
30.2%
50-696401
15.6%
1503513
 
8.5%
503479
 
8.5%

Most occurring characters

ValueCountFrequency (%)
053505
24.3%
949413
22.5%
-34109
15.5%
128321
12.9%
715304
 
7.0%
513393
 
6.1%
412404
 
5.6%
66401
 
2.9%
>3513
 
1.6%
<3479
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number178741
81.3%
Dash Punctuation34109
 
15.5%
Math Symbol6992
 
3.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
053505
29.9%
949413
27.6%
128321
15.8%
715304
 
8.6%
513393
 
7.5%
412404
 
6.9%
66401
 
3.6%
Math Symbol
ValueCountFrequency (%)
>3513
50.2%
<3479
49.8%
Dash Punctuation
ValueCountFrequency (%)
-34109
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common219842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
053505
24.3%
949413
22.5%
-34109
15.5%
128321
12.9%
715304
 
7.0%
513393
 
6.1%
412404
 
5.6%
66401
 
2.9%
>3513
 
1.6%
<3479
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII219842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
053505
24.3%
949413
22.5%
-34109
15.5%
128321
12.9%
715304
 
7.0%
513393
 
6.1%
412404
 
5.6%
66401
 
2.9%
>3513
 
1.6%
<3479
 
1.6%

hectarcoords_anfang
Categorical

HIGH CARDINALITY

Distinct8146
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Memory size321.2 KiB
POINT(2665800 1210400)
 
300
POINT(2665900 1209300)
 
230
POINT(2665800 1210500)
 
206
POINT(2665800 1210900)
 
201
POINT(2666100 1211000)
 
187
Other values (8141)
39977 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters904222
Distinct characters18
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4858 ?
Unique (%)11.8%

Sample

1st rowPOINT(2665900 1211000)
2nd rowPOINT(2665400 1211900)
3rd rowPOINT(2666800 1210200)
4th rowPOINT(2665600 1211300)
5th rowPOINT(2668500 1212100)

Common Values

ValueCountFrequency (%)
POINT(2665800 1210400)300
 
0.7%
POINT(2665900 1209300)230
 
0.6%
POINT(2665800 1210500)206
 
0.5%
POINT(2665800 1210900)201
 
0.5%
POINT(2666100 1211000)187
 
0.5%
POINT(2665900 1210900)183
 
0.4%
POINT(2665500 1211100)176
 
0.4%
POINT(2666000 1211000)174
 
0.4%
POINT(2666300 1210500)168
 
0.4%
POINT(2666000 1210700)163
 
0.4%
Other values (8136)39113
95.2%

Length

2022-09-29T09:26:25.055429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
point(26660001898
 
2.3%
point(26661001683
 
2.0%
point(26659001626
 
2.0%
point(26658001535
 
1.9%
12116001374
 
1.7%
12118001292
 
1.6%
12120001202
 
1.5%
12117001157
 
1.4%
12105001123
 
1.4%
point(26657001115
 
1.4%
Other values (2710)68197
83.0%

Most occurring characters

ValueCountFrequency (%)
0188850
20.9%
2101397
11.2%
693626
10.4%
193493
10.3%
P41101
 
4.5%
I41101
 
4.5%
N41101
 
4.5%
T41101
 
4.5%
(41101
 
4.5%
O41101
 
4.5%
Other values (8)180250
19.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number575414
63.6%
Uppercase Letter205505
 
22.7%
Open Punctuation41101
 
4.5%
Space Separator41101
 
4.5%
Close Punctuation41101
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0188850
32.8%
2101397
17.6%
693626
16.3%
193493
16.2%
521239
 
3.7%
416535
 
2.9%
916179
 
2.8%
315665
 
2.7%
715451
 
2.7%
812979
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
P41101
20.0%
I41101
20.0%
N41101
20.0%
T41101
20.0%
O41101
20.0%
Open Punctuation
ValueCountFrequency (%)
(41101
100.0%
Space Separator
ValueCountFrequency (%)
41101
100.0%
Close Punctuation
ValueCountFrequency (%)
)41101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common698717
77.3%
Latin205505
 
22.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0188850
27.0%
2101397
14.5%
693626
13.4%
193493
13.4%
(41101
 
5.9%
41101
 
5.9%
)41101
 
5.9%
521239
 
3.0%
416535
 
2.4%
916179
 
2.3%
Other values (3)44095
 
6.3%
Latin
ValueCountFrequency (%)
P41101
20.0%
I41101
20.0%
N41101
20.0%
T41101
20.0%
O41101
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII904222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0188850
20.9%
2101397
11.2%
693626
10.4%
193493
10.3%
P41101
 
4.5%
I41101
 
4.5%
N41101
 
4.5%
T41101
 
4.5%
(41101
 
4.5%
O41101
 
4.5%
Other values (8)180250
19.9%

hectarcoords_ende
Categorical

HIGH CARDINALITY

Distinct6823
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Memory size321.2 KiB
POINT(2665900 1210600)
 
399
POINT(2662600 1211100)
 
258
POINT(2665900 1209300)
 
248
POINT(2665800 1210400)
 
246
POINT(2666300 1210500)
 
239
Other values (6818)
39711 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters904222
Distinct characters18
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3364 ?
Unique (%)8.2%

Sample

1st rowPOINT(2665900 1211000)
2nd rowPOINT(2665400 1211900)
3rd rowPOINT(2666800 1210200)
4th rowPOINT(2665600 1211300)
5th rowPOINT(2668500 1212100)

Common Values

ValueCountFrequency (%)
POINT(2665900 1210600)399
 
1.0%
POINT(2662600 1211100)258
 
0.6%
POINT(2665900 1209300)248
 
0.6%
POINT(2665800 1210400)246
 
0.6%
POINT(2666300 1210500)239
 
0.6%
POINT(2666700 1210600)235
 
0.6%
POINT(2666000 1210700)194
 
0.5%
POINT(2666100 1212400)171
 
0.4%
POINT(2666100 1211000)169
 
0.4%
POINT(2665800 1210900)163
 
0.4%
Other values (6813)38779
94.4%

Length

2022-09-29T09:26:25.134688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
point(26659001869
 
2.3%
point(26660001845
 
2.2%
point(26661001640
 
2.0%
12116001287
 
1.6%
point(26658001268
 
1.5%
12106001178
 
1.4%
12118001177
 
1.4%
12105001166
 
1.4%
point(26657001095
 
1.3%
12121001056
 
1.3%
Other values (2310)68621
83.5%

Most occurring characters

ValueCountFrequency (%)
0189520
21.0%
2101372
11.2%
694535
10.5%
192781
10.3%
P41101
 
4.5%
)41101
 
4.5%
I41101
 
4.5%
N41101
 
4.5%
T41101
 
4.5%
(41101
 
4.5%
Other values (8)179408
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number575414
63.6%
Uppercase Letter205505
 
22.7%
Close Punctuation41101
 
4.5%
Open Punctuation41101
 
4.5%
Space Separator41101
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0189520
32.9%
2101372
17.6%
694535
16.4%
192781
16.1%
520790
 
3.6%
916773
 
2.9%
416092
 
2.8%
715643
 
2.7%
314936
 
2.6%
812972
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
P41101
20.0%
I41101
20.0%
N41101
20.0%
T41101
20.0%
O41101
20.0%
Close Punctuation
ValueCountFrequency (%)
)41101
100.0%
Open Punctuation
ValueCountFrequency (%)
(41101
100.0%
Space Separator
ValueCountFrequency (%)
41101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common698717
77.3%
Latin205505
 
22.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0189520
27.1%
2101372
14.5%
694535
13.5%
192781
13.3%
)41101
 
5.9%
(41101
 
5.9%
41101
 
5.9%
520790
 
3.0%
916773
 
2.4%
416092
 
2.3%
Other values (3)43551
 
6.2%
Latin
ValueCountFrequency (%)
P41101
20.0%
I41101
20.0%
N41101
20.0%
T41101
20.0%
O41101
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII904222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0189520
21.0%
2101372
11.2%
694535
10.5%
192781
10.3%
P41101
 
4.5%
)41101
 
4.5%
I41101
 
4.5%
N41101
 
4.5%
T41101
 
4.5%
(41101
 
4.5%
Other values (8)179408
19.8%

Interactions

2022-09-29T09:26:17.191739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:57.513869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:58.883770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:00.398999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:01.880266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:04.062893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:05.471970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:06.895363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:08.304314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:09.723823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:11.500593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:12.969383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:14.403422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:15.819236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:17.286828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:57.608861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:58.984053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:00.497900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:01.978948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:04.159800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:05.569085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:06.991202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:08.406661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:09.822443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:11.598076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:13.067150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:14.503145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:15.913584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:17.392886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:57.711571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:59.093849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:00.609447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:02.086199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:04.274351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:05.674614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:07.095526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:08.512255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:09.936064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:11.706640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:13.174915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:14.609069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:16.022542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:17.498399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:57.814824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:59.200415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:00.721324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:02.192437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:04.382906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:05.778358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:07.198386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:08.617903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:10.045267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:11.812463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:13.279725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:14.713682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:16.126971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:17.599910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:57.916659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:59.315501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:00.840366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:02.299091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:04.490769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:05.887213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:07.303885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:08.720648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:10.155037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:11.928296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:13.385046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:14.818758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:16.229591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:17.692659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:58.010901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:59.420877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:00.947990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:02.397836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:04.582583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:05.983052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:07.404454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:08.817429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:10.256496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:12.026245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:13.487433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:14.917117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:16.321895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:17.789203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:58.104487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:59.528454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:01.050389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:02.502881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:04.678870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:06.080812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:07.501982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:08.915932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:10.662971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:12.125332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:13.585965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:15.015558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:16.417489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:17.884020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:58.199087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:59.634717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:01.153863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:03.327724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:04.774883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:06.181919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:07.598369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:09.015513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:10.768914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:12.224604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:13.683118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:15.111965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:16.513639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:17.981552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:58.298087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:59.746728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:01.256487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:03.429791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:04.870861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:06.280511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:07.695260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:09.110987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:10.869860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:12.325755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:13.782967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:15.208597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:16.606643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:18.084001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:58.398226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:59.863323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:01.368708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:03.537354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:04.975542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:06.392248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:07.796734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:09.220823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:10.984113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:12.440619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:13.890089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:15.312199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:16.707487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:18.186994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:58.500638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:59.976689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:01.476437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:03.645323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:05.078297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:06.496812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:07.904840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:09.329660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:11.090085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:12.554330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:14.003460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:15.419141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:16.808728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:18.289345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:58.597528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:00.081887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:01.576752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:03.746792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:05.177490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:06.598092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:08.007117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:09.430705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:11.192752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:12.658351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:14.105100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:15.522684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:16.904874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:18.387132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:58.695161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:00.188341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:01.678948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:03.851566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:05.275432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:06.699835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:08.107622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:09.529777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:11.292940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:12.760954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:14.206553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:15.622351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:17.004873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:18.481984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:25:58.788751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:00.291161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:01.777825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:03.954567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:05.373986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:06.795621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:08.202290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:09.624619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:11.396938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:12.859399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:14.303042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:15.720039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-29T09:26:17.097917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-09-29T09:26:25.224131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-29T09:26:25.400783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-29T09:26:25.586861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-29T09:26:25.754333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-29T09:26:25.914079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-29T09:26:18.709998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-29T09:26:19.420182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

rjhrpersnrumzugsdistsex_anfangsex_endmaritalstatus_anfangmaritalstatus_endeagegroup_anfangagegroup_endenationalitygroup_anfangnationalitygroup_endegdekt_anfanggdekt_endegdektnr_anfanggdektnr_endegdenr_anfanggdenr_endegdename_anfanggdename_endegkats_anfanggkats_endegbaups_anfanggbaups_endegazwot_anfanggazwot_endewazimsgroup_anfangwazimsgroup_endewareasgroup_anfangwareasgroup_endehectarcoords_anfanghectarcoords_ende
020181282891011445050SwitzerlandSwitzerlandLULU3310611061LuzernLuzern103010308016801624243-41-2100-14950-69POINT(2665900 1211000)POINT(2665900 1211000)
1201857931480221178SwitzerlandSwitzerlandLULU3310611061LuzernLuzern1030102580218021553-45-670-99100-149POINT(2665400 1211900)POINT(2665400 1211900)
2201812209490111134SwitzerlandSwitzerlandLULU3310611061LuzernLuzern1025102580228022993-43-4100-149100-149POINT(2666800 1210200)POINT(2666800 1210200)
320186325643011112530SwitzerlandSwitzerlandLULU3310611061LuzernLuzern1030103080118011551-21-2<5050-69POINT(2665600 1211300)POINT(2665600 1211300)
420181902613011112525Central EuropeCentral EuropeLULU3310611061LuzernLuzern1025102580208020431-23-470-99100-149POINT(2668500 1212100)POINT(2668500 1212100)
52018769632801111910SwitzerlandSwitzerlandLULU3310611061LuzernLuzern102510258020802014195-65-6>150100-149POINT(2666700 1210600)POINT(2666700 1210600)
6201811831750111167SwitzerlandSwitzerlandLULU3310611061LuzernLuzern1025102580228022993-43-4100-149100-149POINT(2666800 1210200)POINT(2666800 1210200)
720183987918022113535SwitzerlandSwitzerlandLULU3310611061LuzernLuzern1030104080128011121-21-2100-149100-149POINT(2665500 1211000)POINT(2665500 1211000)
820182234972011113030SwitzerlandSwitzerlandLULU3310611061LuzernLuzern1025102580208020441-23-450-69100-149POINT(2668700 1212100)POINT(2668700 1212100)
9201821253640221145Central EuropeCentral EuropeLULU3310611061LuzernLuzern1025102180208020413-45-6100-149>150POINT(2668700 1212100)POINT(2668700 1212100)

Last rows

rjhrpersnrumzugsdistsex_anfangsex_endmaritalstatus_anfangmaritalstatus_endeagegroup_anfangagegroup_endenationalitygroup_anfangnationalitygroup_endegdekt_anfanggdekt_endegdektnr_anfanggdektnr_endegdenr_anfanggdenr_endegdename_anfanggdename_endegkats_anfanggkats_endegbaups_anfanggbaups_endegazwot_anfanggazwot_endewazimsgroup_anfangwazimsgroup_endewareasgroup_anfangwareasgroup_endehectarcoords_anfanghectarcoords_ende
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